国际空间站基于Bi-GRU的自动无尘紧急解决方案（AED-ISS）
Automatic Emergency Dust-Free solution on-board International Space Station with Bi-GRU (AED-ISS)
AI总结：
本研究针对国际空间站仪器受颗粒物威胁的问题，提出基于Bi-GRU算法的早期预警系统，通过收集过去90分钟数据预测未来1分钟的颗粒物水平，为宇航员提供反应时间并具备开发为遥感烟雾报警器的潜力。
摘要：
随着对PM2.5或PM0.3问题的日益关注，颗粒物不仅已成为对环境和人类的潜在威胁，也对国际空间站（ISS）上的仪器构成了危害。我们团队旨在将不同浓度的颗粒物与磁场、湿度、加速度、温度、压力和CO2浓度建立关联。我们的目标是建立早期预警系统（EWS），该系统能够预测颗粒物水平，并为宇航员提供充足的反应时间，以在某些实验中保护仪器或提高测量精度；此外，所构建的模型可进一步开发为与火灾相关应用的遥感烟雾报警器原型。在本文中，我们将实施Bi-GRU（双向门控循环单元）算法，收集过去90分钟的数据，并预测未来1分钟内每0.1升空气中直径超过2.5微米的颗粒物水平，这被归类为早期预警。
英文摘要：
With a rising attention for the issue of PM2.5 or PM0.3, particulate matters have become not only a potential threat to both the environment and human, but also a harming existence to instruments onboard International Space Station (ISS). Our team is aiming to relate various concentration of particulate matters to magnetic fields, humidity, acceleration, temperature, pressure and CO2 concentration. Our goal is to establish an early warning system (EWS), which is able to forecast the levels of particulate matters and provides ample reaction time for astronauts to protect their instruments in some experiments or increase the accuracy of the measurements; In addition, the constructed model can be further developed into a prototype of a remote-sensing smoke alarm for applications related to fires. In this article, we will implement the Bi-GRU (Bidirectional Gated Recurrent Unit) algorithms that collect data for past 90 minutes and predict the levels of particulates which over 2.5 micrometer per 0.1 liter for the next 1 minute, which is classified as an early warning
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
